The explosive rise in popularity of smart phones has exposed the capacity limitation of current cellular communications networks. The increasing usage of bandwidth-demanding multimedia and social networking applications on mobile devices further exacerbates the problem. To cope with the exponential growth in wireless data traffic, it is anticipated that substantially denser deployment of access nodes will be required in the future. Such a dense deployment may be achieved by gradually augmenting existing base stations with a much denser mix of “smaller,” or low power, base stations with reduced signal footprints.
Clearly, the feasibility of a very dense deployment of access nodes is predicated on the existence of a backhaul network that can provide high-data-rate transport for each individual access node in the network. From the viewpoint of maximizing capacity, optical-fiber-based backhaul solutions are probably the most desirable ones and are most suitable for new constructions. However, in existing buildings and infrastructure, the cost of installation of new fibers to every access node in a very dense network can be prohibitive.
An alternative to an optical-fiber-based backhaul solution is a wireless self-backhaul solution where the same access spectrum is used to provide transport. A large amount of bandwidth is expected to be available in high frequency bands (e.g., the Millimeter Wave (MMW) bands) where future wireless systems are likely to be deployed. In addition, these future wireless systems have the potential of a high degree of spatial reuse due to the associated reduced radio wavelengths. Both the large amount of available bandwidth and the high degree of spatial reuse motivate the self-backhauling approach. The simplicity of the wireless self-backhaul approach and the potential of substantially reducing the deployment cost also make the wireless self-backhaul approach very attractive.
As illustrated in FIG. 1, in the wireless self-backhaul approach, an access node (AN) provides network access to user equipment devices (UEs) in its vicinity that are assigned to the access node as well as transport for neighboring access nodes. With regard to transport, the access node operates as a relay node in order to route data toward and/or from an aggregation node. A group of self-backhauling access nodes can form a wireless mesh network, where the access nodes cooperatively route each other's traffic to and from the aggregation node, possibly through multiple “hops.” The aggregation node connects the wireless mesh network to a larger network (e.g., a core network of the associated cellular communications network).
Not only does self-backhauling eliminate the need to install additional wires/fibers and hence substantially reduce deployment cost, self-backhauling also provides the ultimate flexibility for users or network operators to deploy access nodes anywhere there is unmet traffic demand. Even in the case when wired (fiber or copper based) backhaul is available, self-backhauling can still serve as a fallback or a diversifying solution to enhance the reliability of the network. However, in order for the self-backhaul solution to be an acceptable substitute for wired backhaul, efficient mechanisms for transferring information from one network node in a wireless mesh network to another network node in the wireless mesh network on a multi-hop route must be designed to ensure adequate end-to-end performance in terms of both data throughput and latency.
Transporting information wirelessly through a wireless mesh network formed by self-backhauling access nodes requires the use of routing algorithms in combination with a routing metric to select which route among all possible routes with one or more hops should be used. Each of these candidate routes consists of an arbitrary but finite number of wireless links, or hops. Common routing algorithms include the Bellman-Ford algorithm and the Dijkstra algorithm, as described in D. P. Bertsekas and R. G. Gallager, “Data Networks,” 2nd Edition, Prentice Hall, 1992. These algorithms typically find the shortest path (or route), in the sense of yielding the best routing metric value, among all possible paths from a source node to a destination node. Once a desirable route has been identified, an appropriate data transmission rate at an ingress node (e.g., an aggregation node or a UE for a wireless mesh backhaul network) can be set accordingly.
To cope with the dynamically varying nature of wireless channels, each access node typically supports a wide range of data transmission rates derived from a set of different possible combinations of Modulation and Coding Schemes (MCSs). To achieve high throughput performance, the appropriate MCS needs to be adaptively selected by each access node for each link depending on its channel condition. Such an adaptive MCS selection process is commonly referred to as link adaptation.
Traditional link adaptation techniques focus on a single communication link and its associated link quality metric. Some existing link adaptation techniques proposed for multi-hop wireless networks are described in E. Yang et al., “An Enhanced Link Adaptation Strategy for IEEE 802.11 Wireless Ad Hoc Networks,” Proc. International Conference on Wireless Communications, Networking and Mobile Computing (WiCom), pp. 1672-1676, September 2007; S. Narayanan et al., “On the advantages of multi-hop extensions to the IEEE 802.111 infrastructure mode,” Proc. WCNC 2005, pp. 132-138, 2005; W. S. Conner et al., “IEEE 802.11s Tutorial—Overview of the Amendment for Wireless Local Area Mesh Networking,” November 2006; G. R. Hiertz et al., “IEEE 802.11s: The WLAN Mesh Standard,” IEEE Wireless Communications, pp. 104-111, February 2010; and G. Holland et al., “A Rate-Adaptive Protocol for Multi-hop Wireless Networks,” Proc. ACM MOBICOM 01, 2001. Existing link adaption strategies proposed for multi-hop wireless networks, such as those referenced above, essentially reuse the same techniques designed for a single communication link. Specifically, each access node on a route selects the MCS that maximizes the data throughput of the individual wireless link with which the access node is concerned. As a result, MCSs supporting different data rates may be selected for different wireless links of a given route due to the differences in the channel and interference conditions among these links, as described in S. Narayanan et al., “On the advantages of multi-hop extensions to the IEEE 802.111 infrastructure mode,” Proc. WCNC 2005, pp. 132-138, 2005 and G. Holland et al., “A Rate-Adaptive Protocol for Multi-hop Wireless Networks,” Proc. ACM MOBICOM 01, 2001. This can lead to packet loss due to buffer overflow at those access nodes whose outgoing data rates are smaller than their respective incoming data rates. Current solutions to this issue rely on flow control and congestion control in the upper layer (e.g., Transmission Control Protocol (TCP)) to back off the transmission rate when the buffer of the receiving network node fills up as described in W. S. Conner et al., “IEEE 802.11s Tutorial—Overview of the Amendment for Wireless Local Area Mesh Networking,” November 2006 and G. R. Hiertz et al., “IEEE 802.11s: The WLAN Mesh Standard,” IEEE Wireless Communications, pp. 104-111, February 2010. This is particularly costly for the multi-hop self-backhaul solution, as any lost packets may have already traversed several hops, and thus can lead to high latency and unnecessary signaling overhead in the wireless network.
In addition to the potential transmission rate mismatch across different hops, the use of single-hop link adaptation techniques in the multi-hop setting does little to account for the potential inter-hop interference from neighboring links of a route in link adaptation. This can result in an incorrect MCS selection that over-estimates the feasible transmission rate in each hop, which in turn leads to additional delay in re-adjustment of transmission rates through upper-layer protocols. Moreover, the imprudent utilization of radio resources in terms of, for example, transmit power, bandwidth, etc. that leads to the excessively high throughputs in some of the links can also create unnecessarily high interference to other links or routes in the wireless network.
In light of the discussion above, link adaptation techniques for a multi-hop route in a wireless mesh network are desired. In addition, there is a need for link adaptation techniques for a multi-hop route in a wireless mesh network that account for interference.